Automated inspection and tallying of man-made markings have widespread applications. For example, in multiple-choice tests, each test taker may be instructed to indicate his or her answer to each question by darkening a delineated area, commonly called a “bubble”, among a row of bubbles on an answer sheet or card (“bubble card” or “scan card”). A bubble card typically contains multiple rows of bubbles for multiple questions, with the bubbles also forming columns. The answer cards are then fed through an optical mark reader (OMR), which optoelectrically detects the location of the darkened bubble in each row, thereby determining the answer that the test taker chose. Similar techniques can also be used in conducting polls and elections.
Traditionally, stringent requirements have been imposed on the behavior of test takers, at least partially because traditional OMRs are not very tolerant of variations in how the bubbles are filled. For example, OMRs typically detect narrow infrared beams reflected from the bubbles and only distinguish between “black” and “white”, i.e., bubbles that are darker or lighter than a particular threshold level. With such devices, the test taker typically must use one type of marker (e.g. number-2 pencil, but not pens, due to the differences in infrared absorption properties) and must fill in the bubbles completely.
Deviation from such requirements often results in misinterpretation of the test taker's intent. For example, incomplete coverage of a bubble may result in the bubble be recognized as a blank; erasures that leave smudges may be mistakenly recognized as filled bubbles. Additionally, because of such onerous requirements, test takers may become preoccupied with filling the bubbles correctly and less able to focus on the substantive tasks at hand. The answers, as recognized by the OMR, therefore may not accurately reflect the true level of the test takers' ability to answer the questions, but rather tend to include distortions caused by the mechanical difficulties of marking the answer sheets.
A traditional OMR typically employs an array light-emitting diodes (LEDs), which shine light on the multiple columns of bubbles on an answer sheet, and a corresponding array of photo detectors for detecting marks by measuring the light reflected from the bubbles. To ensure that similar marks in different columns will generate substantially similar responses among the detectors, prior art OMRs have employed components having closely matched characteristics, or individual potentiometers, manually adjusted at factory, for biasing each LED or detector. The manufacturing process for such OMRs is labor intensive. Such OMRs are also unsuited for recalibration in the field to accommodate changes in the characteristics of the components over time.
It is thus desirable to create an OMR is capable of correctly discerning test takers' intent from a wider variety of marks than the traditional OMRs and is more conveniently calibrated.